138 citations · 265 across the 7 of their papers we have counts for
7 papers
From ADP to the Brain: Foundations, Roadmap, Challenges and Research Priorities
Paul J Werbos
This paper defines and discusses Mouse Level Computational Intelligence (MLCI) as a grand challenge for the coming century. It provides a specific roadmap to reach that target, cit…
Complete stability analysis of a heuristic ADP control design
Yury Sokolov, Robert Kozma, Ludmilla D. Werbos +1
This paper provides new stability results for Action-Dependent Heuristic Dynamic Programming (ADHDP), using a control algorithm that iteratively improves an internal model of the e…
Brain-Like Stochastic Search: A Research Challenge and Funding Opportunity
Paul J. Werbos
Brain-Like Stochastic Search (BLiSS) refers to this task: given a family of utility functions U(u,A), where u is a vector of parameters or task descriptors, maximize or minimize U…
Beyond Feedforward Models Trained by Backpropagation: a Practical Training Tool for a More Efficient Universal Approximator
Roman Ilin, Robert Kozma, Paul J. Werbos
Cellular Simultaneous Recurrent Neural Network (SRN) has been shown to be a function approximator more powerful than the MLP. This means that the complexity of MLP would be prohibi…
Using Adaptive Dynamic Programming to Understand and Replicate Brain Intelligence: the Next Level Design
Paul J. Werbos
Since the 1960s I proposed that we could understand and replicate the highest level of intelligence seen in the brain, by building ever more capable and general systems for adaptiv…
Stable Adaptive Control Using New Critic Designs
Paul J. Werbos
Classical adaptive control proves total-system stability for control of linear plants, but only for plants meeting very restrictive assumptions. Approximate Dynamic Programming (AD…